Automated Bioanalytical Workflow for Ligand Binding-Based Pharmacokinetic Assay Development

The growth of therapeutic monoclonal antibodies (mAbs) continues to accelerate due to their success as treatments for many diseases. As new therapeutics are developed, it is increasingly important to have robust bioanalytical methods to measure the pharmacokinetics (PK) of circulating therapeutic mAbs in serum. Ligand-binding assays such as enzyme-linked immunosorbent assays (ELISAs) with anti-idiotypic antibodies (anti-IDs) targeting the variable regions of the therapeutic antibody are sensitive and specific bioanalytical methods to measure levels of therapeutic antibodies in a biological matrix. However, soluble circulating drug mAb targets can interfere with the anti-IDs binding to the therapeutic mAb, thereby resulting in an underestimation of total drug concentration. Therefore, in addition to a high binding affinity for the mAb, the selection of anti-IDs and the assay format that are not impacted by soluble antigens and have low matrix interference is essential for developing a robust PK assay. Standardized automated approaches to screen and select optimal reagents and assay formats are critical to increase efficiency, quality, and PK assay robustness. However, there does not exist an integrated screening and analysis platform to develop robust PK assays across multiple formats. We have developed an automated workflow and scoring platform with multiple bioanalytical assay parameters that allow for ranking of candidate anti-IDs. A primary automated indirect electrochemiluminescence (ECL) was utilized to shortlist the anti-IDs that were selected for labeling and screening in pairs. A secondary screen using an ECL sandwich assay with labeled-anti-ID pairings was used to test multiple PK assay formats to identify the best anti-ID pairing/PK assay format. We developed an automated assay using fixed plate maps combined with a human-guided graphical user interface-based scoring system and compared it to a data-dependent scoring system using Gaussian mixture models for automated scoring and selection. Our approach allowed for screening of anti-IDs and identification of the most robust PK assay format with significantly reduced time and resources compared with traditional approaches. We believe that such standardized, automated, and integrated platforms that accelerate the development of PK assays will become increasingly important for supporting future human clinical trials.